文献类型：会议论文

英文题名：Community Intrusion Detection System Based on Radial Basic Probabilistic Neural Network

作者：Gao, Meijuan[1];Tian, Jingwen[1];Zhou, Shiru[1]

第一作者：高美娟

通讯作者：Gao, MJ[1]

机构：[1]Beijing Union Univ, Coll Automat, Beijing 100101, Peoples R China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Automat, Beijing 100101, Peoples R China.|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：6th International Symposium on Neural Networks

会议日期：MAY 26-29, 2009

会议地点：Wuhan, PEOPLES R CHINA

语种：英文

外文关键词：Community; Intrusion Detection; Radial Basic Probabilistic Neural Network; Face Recognition

摘要：A community intrusion detection system based on radial basic probabilistic neural network (RBPNN) is presented in this paper. This system is composed of ARM (Advanced RISC Machines) data acquisition nodes, wireless mesh network and control centre. The sensor is used to collect information in the data acquisition node and processes them by image detection algorithm, and then transmits information to control centre with wireless mesh network. When there is abnormal phenomenon, the system starts the camera and the radial basic probabilistic neural network algorithm is Used to recognize the face image. We construct the structure of RBPNN that used for recognition face image and adopt the K-Nearest Neighbor algorithm and least square method to train the network. With the ability of strong pattern classification and function approach and fast convergence of RBPNN. the recognition method can truly classify the face. This system resolves the defect and improves the intelligence and alleviates worker's working stress.